Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolu...

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Autores principales:	Wentong Wu, Han Liu, Lingling Li, Yilin Long, Xiaodong Wang, Zhuohua Wang, Jinglun Li, Yi Chang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/82c2da1bc3904a87838e4ad3a9441368
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spelling	oai:doaj.org-article:82c2da1bc3904a87838e4ad3a94413682021-11-04T07:42:06ZApplication of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image1932-6203https://doaj.org/article/82c2da1bc3904a87838e4ad3a94413682021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555847/?tool=EBIhttps://doaj.org/toc/1932-6203This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.Wentong WuHan LiuLingling LiYilin LongXiaodong WangZhuohua WangJinglun LiYi ChangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wentong Wu Han Liu Lingling Li Yilin Long Xiaodong Wang Zhuohua Wang Jinglun Li Yi Chang Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
description	This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.
format	article
author	Wentong Wu Han Liu Lingling Li Yilin Long Xiaodong Wang Zhuohua Wang Jinglun Li Yi Chang
author_facet	Wentong Wu Han Liu Lingling Li Yilin Long Xiaodong Wang Zhuohua Wang Jinglun Li Yi Chang
author_sort	Wentong Wu
title	Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
title_short	Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
title_full	Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
title_fullStr	Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
title_full_unstemmed	Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image
title_sort	application of local fully convolutional neural network combined with yolo v5 algorithm in small target detection of remote sensing image
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/82c2da1bc3904a87838e4ad3a9441368
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_version_	1718445032830140416

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

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